{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/main/examples/sklearn/demo_reject_option_classification.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reject Option based Classification\n",
    "\n",
    "Reject Option based Classification (ROC) is a simple but powerful post-processing method which works by simply assigning favorable outcomes to members of the unprivileged group and unfavorable outcomes to members of the privileged groups when the prediction probability from an estimator is within a \"critical region\" of ambiguity. We parameterize this critical region with a threshold (center) and margin (radius). We can then run a naive search over all combinations of these parameters to find the best tradeoff between predictive performance and fairness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Install AIF360\n",
    "!pip install 'aif360' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import tri\n",
    "from mpl_toolkits import mplot3d\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from aif360.sklearn.postprocessing import RejectOptionClassifierCV, PostProcessingMeta\n",
    "from aif360.sklearn.datasets import fetch_adult\n",
    "from aif360.algorithms.postprocessing.reject_option_classification import _get_pareto_frontier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fetch Adult dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y, sample_weight = fetch_adult()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode protected attributes, labels, and categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.index = pd.MultiIndex.from_arrays(X.index.codes, names=X.index.names)\n",
    "y.index = pd.MultiIndex.from_arrays(y.index.codes, names=y.index.names)\n",
    "y = pd.Series(y.factorize(sort=True)[0], index=y.index)\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can combine our ROC grid search with a logistic regression classifier using the meta-estimator class to complete the whole process in one step. Note: `RejectOptionClassifierCV` does not actually perform true cross-validation since this algorithm does not \"learn\" anything but this class will perform the grid search and, optionally, \"refit\" the post-processor using the optimal threshold and margin (best balanced accuracy given fairness score >= 0.8 for disparate impact or >= -0.1 for all other scorers). The `n_jobs` argument specifies the number of parallel processes to use (-1 means use all CPUs). By using the meta-estimator, we ensure the data used to fit the post-processor is a held-out validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'margin': 0.12000000000000001, 'prot_attr': 'sex', 'threshold': 0.18}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = 'disparate_impact'\n",
    "pp = PostProcessingMeta(LogisticRegression(solver='liblinear'),\n",
    "        RejectOptionClassifierCV('sex', scoring=metric, step=0.02, n_jobs=-1))\n",
    "pp.fit(X, y)\n",
    "pp.postprocessor_.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the results of the grid search in 2D and look at the Pareto front (cyan) for the tradeoff between balanced accuracy and disparate impact. The starred point represents the best params chosen by the refitting protocol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = pd.DataFrame(pp.postprocessor_.cv_results_)\n",
    "ax = res.plot.scatter('mean_test_'+metric, 'mean_test_bal_acc', c='gray', marker='.')\n",
    "\n",
    "all_pts = res[['mean_test_'+metric, 'mean_test_bal_acc']].to_numpy()\n",
    "pareto_idx = _get_pareto_frontier(all_pts)\n",
    "ax.scatter(all_pts[pareto_idx, 0], all_pts[pareto_idx, 1], c='cyan')\n",
    "\n",
    "refit_idx = pp.postprocessor_.best_index_\n",
    "ax.scatter(all_pts[refit_idx, 0], all_pts[refit_idx, 1], s=100, c='magenta', marker='*')\n",
    "\n",
    "ba = res.mean_test_bal_acc\n",
    "ax.fill_betweenx([ba.min()-0.05, ba.max()+0.05], 1, 0.8, color='g', alpha=0.1)\n",
    "ax.fill_betweenx([ba.min()-0.05, ba.max()+0.05], 0.8, color='r', alpha=0.1)\n",
    "ax.margins(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the 4D surface of our choices of threshold and margin and their corresponding balanced accuracy and disparate impact by plotting in 3D and using color to represent the fourth dimension. Again, the Pareto front points and \"best\" choice are shown in cyan and magenta, respectively (although they may be covered up by the surface plot from some angles). We can see that increasing the margin improves fairness but past a certain point it begins to overcompensate. We can also see that, in this case, lower thresholds generally lead to better balanced accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "ax = f.add_subplot(projection='3d')\n",
    "surf = ax.plot_trisurf(res.param_margin, res.param_threshold, res.mean_test_bal_acc, cmap='PiYG', alpha=0.75)\n",
    "triangles = tri.Triangulation(res.param_margin, res.param_threshold).triangles\n",
    "colors = res.mean_test_disparate_impact.to_numpy().take(triangles).max(axis=1)\n",
    "surf.set_array(colors)\n",
    "f.colorbar(surf, shrink=0.75, label='disparate impact');\n",
    "\n",
    "pareto_pts = res.iloc[pareto_idx]\n",
    "ax.scatter(pareto_pts.param_margin, pareto_pts.param_threshold, pareto_pts.mean_test_bal_acc, c='cyan')\n",
    "\n",
    "best_pt = res.loc[pp.postprocessor_.best_index_]\n",
    "ax.scatter(best_pt.param_margin, best_pt.param_threshold, best_pt.mean_test_bal_acc,\n",
    "           c='magenta', marker='*', s=100)\n",
    "\n",
    "ax.set_xlabel('margin')\n",
    "ax.set_ylabel('threshold')\n",
    "ax.set_zlabel('bal acc')\n",
    "ax.view_init(30, 30)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
